A new topological clustering algorithm for interval data

被引：25

作者：

Cabanes, Guenael ^{[1
]}

Bennani, Younes ^{[1
]}

Destenay, Renaud ^{[2
,3
]}

Hardy, Andre ^{[2
,3
]}

机构：

[1] Univ Paris 13, CNRS, UMR 7030, LIPN, F-93430 Villetaneuse, France

[2] Univ Namur FUNDP, Namur Ctr Complex Syst naXys, B-5000 Namur, Belgium

[3] Univ Namur FUNDP, Dept Math, B-5000 Namur, Belgium

来源：

PATTERN RECOGNITION | 2013年 / 46卷 / 11期

关键词：

Interval data; Clustering; Self-organizing map;

D O I：

10.1016/j.patcog.2013.03.023

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Clustering is a very powerful tool for automatic detection of relevant sub-groups in unlabeled data sets. In this paper we focus on interval data: i.e., where the objects are defined as hyper-rectangles. We propose here a new clustering algorithm for interval data, based on the learning of a Self-Organizing Map. The major advantage of our approach is that the number of clusters to find is determined automatically; no a priori hypothesis for the number of clusters is required. Experimental results confirm the effectiveness of the proposed algorithm when applied to interval data. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：3030 / 3039

页数：10

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